10th International Congress on Information and Communication Technology

Authors - Pei-Yi Hao
Abstract - Most stock prediction models rely on classification or regression methods to forecast stock price trends or prices, with their primary goal being to enhance the fit between predicted results and actual values rather than directly identifying the best investment targets. Consequently, the stocks recommended by these models may not necessarily yield the optimal returns. In contrast, stock ranking prediction provides a more direct and effective approach to portfolio construction by forecasting the ranking sequence of stock returns (with higher-return stocks ranked higher). This process is referred to as stock selection. The key to stock selection lies in identifying stocks that are most likely to help investors generate profits. Since stock prediction involves different tasks such as classification, regression, and ranking, which exhibit significant interrelations, most deep learning algorithms tend to train these tasks independently, overlooking their correlations. However, these related tasks may share underlying knowledge, which should be jointly learned to maximize the utilization of the potential information behind each task. Support vector machines (SVMs) have demonstrated exceptional performance in multi-task learning and have achieved success in numerous practical applications. This paper proposes a novel multitask support vector machine capable of simultaneously learning classification, regression, and ranking models. By leveraging the correlations among these tasks, the proposed framework aims to improve the predictive performance of each individual task.